Adaptive Biasing Cell Association in FFR Aided Multi-tier Heterogeneous Networks under Dynamic Load Variation
Abstract
Heterogeneous networks (HetNets) adopting fractional frequency reuse (FFR) improves cell coverage, network capacity, efficiency, assures higher data rates, and better quality of service (QoS) for next generation wireless networks. However they fail to handle dynamic load variation. So we attempt biasing cell association (BCA) to offload user from macrocell to small cell base stations (SCBs) to overcome capacity reduction and throughput degradation. It is based on range expansion of SCBSs by adding a positive bias to the reference signal received power (RSRP). In this paper we propose a FFR aided twin layer HetNet with an adaptive biasing scheme for load balancing. For users offloading a cell is selected by self-organizing network (SON) with adaptive bias value using Q-learning algorithm. Simulation result show that our system model can handle dynamic load variation with proper utilization of available bandwidth and mitigate interference better than the conventional HetNet design.